THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES MAY NOT BE TRUSTWORTHY FOR SOME PARAMETERS DUE TO A NON-POSITIVE DEFINITE FIRST-ORDER DERIVATIVE PRODUCT MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS 0.176D-13. PROBLEM INVOLVING PARAMETER 23.

The results are not valid if a model is not identified. If you ask for TECH1 in the OUTPUT command, you can see what parameter 23 is. If this does not help, please send your input, data, output, and license number to support@statmodel.com.

Parameter 23 is the value in the PSI matrix corresponding to aainv1 vs. aainv1, which appears to be the variance of that variable. I don't see anything unusual about the variance of aainv1 (0.234) compared to the others in the SAMPSTAT output or the MODEL output. Do you have any suggestions, or should I email my files for additional help?

I have a similar problem as described above. I asked for TECH1 and found out the parameter is edu vs. edu (covariance coverage=0.935). Are the results still trustworthy? If not, how can I resolve this issue?

I am having a similar problem when I weight my data. That is, the model converges fine with unweighted data, but when I add the weight, I get the same error message:

THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES MAY NOT BE TRUSTWORTHY FOR SOME PARAMETERS DUE TO A NON-POSITIVE DEFINITE FIRST-ORDER DERIVATIVE PRODUCT MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS 0.209D-10. PROBLEM INVOLVING PARAMETER 114.

If the results were fine with unweighted data, can I trust the weighted results?

I'm having a similar problem. I am running a multigroup analysis. The warning also points to the variance of one of my variables. I looked at the descriptives for this variable and everything looks ok, but there is a lot of missing data on this variable. Could the amount of missing data cause this problem? Is there a way that I can still include this variable in my model? It is a very important control. Thanks!

I am testing a sequential mediation model with Mplus. However, when I reverse my model (to say something about causality), I get the following error message:

MAXIMUM LOG-LIKELIHOOD VALUE FOR THE UNRESTRICTED (H1) MODEL IS -5603.305

THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES MAY NOT BE TRUSTWORTHY FOR SOME PARAMETERS DUE TO A NON-POSITIVE DEFINITE FIRST-ORDER DERIVATIVE PRODUCT MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS 0.236D-12. PROBLEM INVOLVING PARAMETER 45.

It does run my model, but my reversed model indicates better fit, which makes no sense theoretically. Is it possible this error message influences my results?

I have gotten the same error as discussed above for a CFA conducted with a clustering variable. THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES MAY NOT BE TRUSTWORTHY FOR SOME PARAMETERS DUE TO A NON-POSITIVE DEFINITE FIRST-ORDER DERIVATIVE PRODUCT MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS -0.260D-17. PROBLEM INVOLVING PARAMETER 23

In addition, my output said: THIS IS MOST LIKELY DUE TO HAVING MORE PARAMETERS THAN THE NUMBER OF CLUSTERS MINUS THE NUMBER OF STRATA WITH MORE THAN ONE CLUSTER.

I entered the cluster variable to account for nesting in my data but will not be including any between-level specifications. To test if the issue is in fact the cluster size, I ran the same CFA without the cluster ID (or the type=COMPLEX command) and the entire error message disappeared. It seems I received this message because of my cluster size (n = 23) being smaller than the number of freed parameters (i.e., 31) but what I do not understand is why this error message would also indicate an issue with a parameter (which in this case was the psi of an observed variable). Are the number of freed parameters actually the problem? And if so, how do I solve this problem?

The number of clusters is the number of independent observations in your data set. The warning is telling you that you have more parameters than you have independent observations. The impact of this on the results has not been studied. This is simply a warning.

I am running a path model with three latent "predictors", a single outcome, and 4 covariates (age, gender, two dummy codes for ethnicity). When I run the path model without any of the covariates, my model converges and achieves adequate fit. However, when I include paths from each of the 4 covariates to the outcome variable, I recieve the following error messages:

THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES MAY NOT BE TRUSTWORTHY FOR SOME PARAMETERS DUE TO A NON-POSITIVE DEFINITE FIRST-ORDER DERIVATIVE PRODUCT MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS 0.316D-18. PROBLEM INVOLVING PARAMETER 52.

WARNING: THE LATENT VARIABLE COVARIANCE MATRIX (PSI) IS NOT POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL VARIANCE FOR A LATENT VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE BETWEEN TWO LATENT VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO LATENT VARIABLES. CHECK THE TECH4 OUTPUT FOR MORE INFORMATION. PROBLEM INVOLVING VARIABLE PGENDER.

There are no negative residual variances, correlations greater than 1, etc. Also, with parameter 52, this is a covariance between PGENDER and PAGE, and I cannot find a problem in the data. Can you make any recommendations about how to proceed? Thanks!

I am facing the same problem, in a GMM with 5 measurement points, trying to run a model with 3 classes.

THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES MAY NOT BE TRUSTWORTHY FOR SOME PARAMETERS DUE TO A NON-POSITIVE DEFINITE FIRST-ORDER DERIVATIVE PRODUCT MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS 0.937D-11. PROBLEM INVOLVING PARAMETER 19.

TECH1 output looks normal, as does TECH4 (no PSI problems). The solution that MPLUS finds is implausible for this model, so I would need to explore what causes these issues.

Hi I am doing 2level, different time points nested within individuals. I have 3 items. My Mplus commands include cluster = firmid; within = time; Analysis: Type = twolevel; Model: %within% RD by exp newcar oldcar; If I run CFA with 2 items, I get: THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES MAY NOT BE TRUSTWORTHY FOR SOME PARAMETERS DUE TO A NON-POSITIVE DEFINITE FIRST-ORDER DERIVATIVE PRODUCT MATRIX If I run CFA with 3 items, I get: THE MODEL ESTIMATION DID NOT TERMINATE NORMALLY DUE TO AN ILL-CONDITIONED FISHER INFORMATION MATRIX. CHANGE YOUR MODEL AND/OR STARTING VALUES. THE MODEL ESTIMATION DID NOT TERMINATE NORMALLY DUE TO A NON-POSITIVE DEFINITE FISHER INFORMATION MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS -0.562D-10. THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES COULD NOT BE COMPUTED. THIS IS OFTEN DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. CHANGE YOUR MODEL AND/OR STARTING VALUES. PROBLEM INVOLVING PARAMETER 5. What is the problem? Thank you.

I have a similar problem. I have received the error: THE MODEL ESTIMATION TERMINATED NORMALLY THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES MAY NOT BE TRUSTWORTHY FOR SOME PARAMETERS DUE TO A NON-POSITIVE DEFINITE FIRST-ORDER DERIVATIVE PRODUCT MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS -0.148D-09. PROBLEM INVOLVING PARAMETER 149.

I’m running a multigroup model and this is the free model. This error did not occur for the constrained model. I believe this parameter is the disturbance covariance for two of the outcome variables. Would you please tell me how this affects my results?

I'm running a basic regression model with an interaction. I'm also using auxiliary variables. I received the following error message when I tried to probe the interaction.

THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES MAY NOT BE TRUSTWORTHY FOR SOME PARAMETERS DUE TO A NON-POSITIVE DEFINITE FIRST-ORDER DERIVATIVE PRODUCT MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS 0.227D-10. PROBLEM INVOLVING PARAMETER 13. THIS IS MOST LIKELY DUE TO VARIABLE M11WORKR BEING DICHOTOMOUS BUT DECLARED AS CONTINUOUS.

Parameter 13 is the variance of my interaction term. M11WORKR is one of my auxiliary variables. I have it listed using the command auxiliary = (m). It is dichotomous, but I'm not sure where in my syntax it is declared as continuous.

Variables on the AUXILIARY list are treated as continuous variables. If a variable on this list is binary, it can prompt the message above because the mean and variance of a binary variable are not orthogonal. If this is the cause of the message, you can ignore it.

All variables are assumed to be continous unless they are put on a list like CATEGORICAL, CENSORED, COUNT etc.

I have received the error: THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES MAY NOT BE TRUSTWORTHY FOR SOME PARAMETERS DUE TO A NON-POSITIVE DEFINITE FIRST-ORDER DERIVATIVE PRODUCT MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS -0.102D-15. PROBLEM INVOLVING PARAMETER 1.

I have checked parameter 1 - it's on the NU matrix, but I'm not sure what this means. Can you please help?

I'm running into a similar issue. When running a fairly simple path analytic model, I get this message:

THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES MAY NOT BE TRUSTWORTHY FOR SOME PARAMETERS DUE TO A NON-POSITIVE DEFINITE FIRST-ORDER DERIVATIVE PRODUCT MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS0.202D-19.PROBLEM INVOLVING PARAMETER 99.

Parameter 99 is the variance of one of the variables in the model,and is rather large (as it should be). The model still runs, and standard errors are still estimated. Is this message ignorable? If not do you have suggestions for how to handle this issue?

I encountered a NPD matrix recently while fitting a structural model with composite variables. The offending factor had a small negative residual variance. I addressed the problem, on the advice of a colleague, with this syntax: factor@.00001; which eliminated the NDP, and appeared to have little or no effect on the fit of the model.

Looking through the user's manual, I can't figure out what exactly this syntax means, why it seems to serve this function, and whether it is appropriate. If it is not appropriate, can you suggest another means of addressing small negative residual variances?

Hello, I'm running a multigroup model with dummy variables as predictors. One of my groups is very small (n= 14). I received this error. Does this mean by results are not valid and I cannot perform a multigroup tests?

Thank you,

Danyel

THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES MAY NOT BE TRUSTWORTHY FOR SOME PARAMETERS DUE TO A NON-POSITIVE DEFINITE FIRST-ORDER DERIVATIVE PRODUCT MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS -0.128D-17. PROBLEM INVOLVING PARAMETER 24.

THIS IS MOST LIKELY DUE TO HAVING MORE PARAMETERS THAN THE SAMPLE SIZE IN ONE OF THE GROUPS.

Hi, I am having a similar problem as many of the previous posters in terms of the following error messages:

THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES MAY NOT BE TRUSTWORTHY FOR SOME PARAMETERS DUE TO A NON-POSITIVE DEFINITE FIRST-ORDER DERIVATIVE PRODUCT MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS -0.149D-17. PROBLEM INVOLVING PARAMETER 54. THE NONIDENTIFICATION IS MOST LIKELY DUE TO HAVING MORE PARAMETERS THAN THE NUMBER OF CLUSTERS. REDUCE THE NUMBER OF PARAMETERS."

I have checked the parameter and still can't make sense of the error message.

You most likely have more parameters than clusters. Check that. This warning is reminding you that independence of observations is at the cluster level. The effect of having more parameters than clusters may have an effect on the results.

Thank you for the reply. I do have about 63 parameters and 52 clusters. Does this affect only the parameter estimates (due to standard errors) or does this influence the chi squared tests and fit indices as well?

I recall my mplus instructor suggesting that one may use Bayes to get more accurate parameter estimates. In the case that I cannot get more clusters, is it acceptable to report the chi-squared results and fit indices from the ML solution and the parameter estimates from the Bayes solution?

Thank you. It seems like my options are to increase the number of clusters in my data or reduce the number of parameters estimated (difficult in this case because of the theoretical model of interest).

How does Mplus check if a given matrix (in this case the Psi matrix) is positive definite?

While running a simulation designed to test for model identification (using Bayes estimation) I ran into the following note from Mplus about 200 times:

THE MODEL ESTIMATION DID NOT TERMINATE NORMALLY. THE POSTERIOR COVARIANCE MATRIX FOR PSI IS NOT POSITIVE DEFINITE, AS IT SHOULD BE.

THE PROBLEM OCCURRED IN CHAIN 1.

For each of the 200, I extracted the most recent update of values corresponding to the PSI matrix of the respective chain (either chain 1 or 2). Then I performed a series of tests:

First: All Psi matrices were full rank, and none contained negative variances on the the diagonal.

50 contained negative eigen values.

Many of the remaining 150 contained small eigen values (e.g. .000000001), so I imagine Mplus (given double precision) rounds this to 0 and assumes an eigen value equal to 0.

BUT in some of those 150, the smallest eigen value was positive and (somewhat) large. For example, one replication had the smallest eigen value of .003. Furthermore, the determinants of the remaining 150 replications were also positive.

Therefore, I am wondering how Mplus determines if a given matrix is positive definite. Would it round an eigen value of .003 to 0?

I ran the Latent interaction to see if there is moderating effects, resulting in no significant moderating effects. And there was warning. I tried to delete or make correlation with other indicators and then the other parameters were problematic.

THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES MAY NOT BE TRUSTWORTHY FOR SOME PARAMETERS DUE TO A NON-POSITIVE DEFINITE FIRST-ORDER DERIVATIVE PRODUCT MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS -0.902D-18. PROBLEM INVOLVING PARAMETER 27.

I used FIML imputation and ran it, resulting in significant moderating effects and no such a warning. The reason of FIML imputation was the moderator indicators has lots of missing data. Would you answer following questions.

(1) Would you explain to me why a warning of non-positive derivative matrix appeared. (2) If there is a solution, would you tell me? (3) Would you tell me which one I should report either the result without imputation or the result with FIML imputation? If there is no problem with FIML imputation. (4) If the missing values are large, the moderating effects are not trustworthy? (5) If so, would you tell me the criteria of percentage of missing values in the dataset.

If you can do FIML without imputation, I think that is preferable. So you want to understand why you get the warning you show here. You can send your output, data, and license number to Support. But if you want, you can also send the imputation run.

When running a two-level model with random slopes, I have received the following error on my output:

THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES MAY NOT BE TRUSTWORTHY FOR SOME PARAMETERS DUE TO A NON-POSITIVE DEFINITE FIRST-ORDER DERIVATIVE PRODUCT MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS 0.778D-10. PROBLEM INVOLVING THE FOLLOWING PARAMETER: Parameter 13, %BETWEEN%: S2 ON GXRQ

Parameter 13 is an interaction term between a binary and continuous variable. This error seems similar to Danielle Roubinov's issue posted on February 1, 2013. Unlike that situation, however, I have not specified any auxiliary variables. How might I know whether or not this warning can be ignored?

Hello Drs. Muthen, I’ve received the following error on my output: THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES MAY NOT BE TRUSTWORTHY FOR SOME PARAMETERS DUE TO A NON-POSITIVE DEFINITE FIRST-ORDER DERIVATIVE PRODUCT MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS 0.137D-19. PROBLEM INVOLVING THE FOLLOWING PARAMETER: Parameter 33, NOCHILD

MODEL COMMAND WITH FINAL ESTIMATES USED AS STARTING VALUES nochild WITH age*-0.54404; nochild WITH house*-0.03903; nochild WITH phyabuse*-0.00280; nochild WITH age1st*-0.15714; nochild WITH reftx*-0.01356;

I’m running a basic logistic regression with 6 variables. Can you please help identify the problem? Thank you

I received the following error on my output and I am not sure if I can trust it: THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES MAY NOT BE TRUSTWORTHY FOR SOME PARAMETERS DUE TO A NON-POSITIVE DEFINITE FIRST-ORDER DERIVATIVE PRODUCT MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS 0.103D-32. PROBLEM INVOLVING THE FOLLOWING PARAMETER: Parameter 1, %WITHIN%: [ DWEVI ]

This is the model I entered. I am not sure why I get this error. There is nothing wrong with the DWEVI parameter.

Thanks for your prompt response. I group mean centred the variable based on previous studies. For example: Binnewies, Sonnentag, & Mojza 2010 stated in their study they the following:

We had data at two levels: the person level (Level 2) and the week level (Level 1). Week-level data were nested within persons. We used the MLwiN software (Rasbash et al., 2000) to analyse the data with hierarchical linear modelling (Snijders & Bosker, 1999). To test for indirect effects of recovery experiences during the weekend on weekly performance, we applied multi-level structural equation modelling to our data (Mehta & Neale, 2005) using the Mplus software (Muthén & Muthén, 2006). To test our hypotheses, person-level predictor variables were centred around the grand mean while week-level predictor variables were centred around the respective person mean (group mean centring). We applied group mean centring because we were interested in within-person relations.

In my study, I am also interested in within person relations. I used ESM to collect data for 3 different moments within a day, and these days are nested within individuals. I want to examine the effect that different demands have on engagement, which is all happening at the within moment level. Based on that reason, I also centred my predictors. However, is there a better way to do it? I appreciate your help!

I jus realised that the error is gone, but in the manual it is stated on chapter 9 pg.261 that "The WITHIN option is used to identify the variables in the data set that are measured on the individual level and modeled only on the within level. They are specified to have no variance in the between part of the model."

However, my variables have variance in the between part. I am not interested in these relations, but by group mean centring I was controlling for this between level variability. However, if I just leave my indicators in the within option part without being group-mean centred (otherwise I get an error as they are indicators of a within latent variable) would this between person variability would be controlled for? I am not sure how else would I be able to control for this between variability?

Great! Thanks! I did the following just to make sure I did it correctly following the instruction from the UG. All the variables (except resp_nr TWE) are measure at different moments asking people about how they feel right now. Thus they are nested within the person (resp_nr). I did not specify within for them, but created their respective latent variable at the between and within level. Thus, expecting Mplus to person centred them implicitly without me specifying it. Thus, may I assume now that the between and within variability are taken care of? I did not get any errors I am not interested in their relations at the between level, except for the relation between momentary engagement and trait engagement, which I model by BWE ON TWE.

However, I am unable to get standardised estimates, is there a way to obtain them with another command?

Also, I am not sure I can completely understand this reasoning (below). I set the residuals to zero because on chapter 9 pg 276 it was stated that: In this model, the residual variances for the factor indicators in the between part of the model are fixed at zero. If factor loadings are constrained to be equal across the within and the between levels, this implies a model where the regression of the within factor on x1 and x2 has a random intercept varying across the clusters.

Is there an article that can explain this a bit more in depth? I followed the procedure, but I would like to still understand why setting the residual variances to zero at the between level constrains the factor loadings to be equal across levels and provide a random intercept at the moment level regression?

I looked it up and found articles about invariance testing, negative residuals, small sample sizes, but I am not sure if these are in line with the manual's purpose of setting the residuals to zero at the between level. Thought it would be better to ask you this instead.

Dear Linda, I am trying to model a basic cross-section data on a binary outcome. Please see below:

I however get the response: =================================== ONE OR MORE PARAMETERS WERE FIXED TO AVOID SINGULARITY OF THE INFORMATION MATRIX. THE SINGULARITY IS MOST LIKELY BECAUSE THE MODEL IS NOT IDENTIFIED, OR BECAUSE OF EMPTY CELLS IN THE JOINT DISTRIBUTION OF THE CATEGORICAL VARIABLES IN THE MODEL. THE FOLLOWING PARAMETERS WERE FIXED: Parameter 30, S ON EXP Parameter 31, S ON EV Parameter 32, S ON BT Parameter 34, [ S$1 ] ==================================== I also observe from the output that the betas are very large apart from those that are fixed.

I am running a model with TYPE=IMPUTATION and TECH9 reports the following error for dataset 4 (with the other datasets the estimation terminated normally):

THE ESTIMATED BETWEEN COVARIANCE MATRIX COULD NOT BE INVERTED. COMPUTATION COULD NOT BE COMPLETED IN ITERATION 146. CHANGE YOUR MODEL AND/OR STARTING VALUES.

I tried the same analysis with dataset 4 only, in order to search for problematic parameters. But with dataset 4 only, the model estimation terminated normally (and ends with iteration 241). Do you have any suggestions how to fix the MI-analysis?

Relevant background information might be: - The five datasets contain identical information (and still have missings) except for two variables which are plausible value data. I randomly assigned the 5 PVs per variable to one of the five datasets. - I am running a TWOLEVEL path model with two variables being categorical (one DV and one mediator) and all others being continuous. - I use weights on both levels and INTEGRATION = MONTECARLO. - The only part I changed is the DATA-Statement (dataset 4 only vs. MI-dat-file + TYPE=IMPUTATION).

It is impossible to say what is happening without see in the outputs and data. Please send them and your license number to support@statmodel.com. Be sure you have run this with Version 7.3 as a first step.

I'm running a longitudinal SEM model based on data collected from students at 17 sites (schools). I'd like to use type=complex to adjust for site influence but when I do, I get the following error:

THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES MAY NOT BE TRUSTWORTHY FOR SOME PARAMETERS DUE TO A NON-POSITIVE DEFINITE FIRST-ORDER DERIVATIVE PRODUCT MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS -0.264D-16. PROBLEM INVOLVING THE FOLLOWING PARAMETER: Parameter 21, POS2 WITH POS1

THIS IS MOST LIKELY DUE TO HAVING MORE PARAMETERS THAN THE NUMBER OF CLUSTERS MINUS THE NUMBER OF STRATA WITH MORE THAN ONE CLUSTER.

I dug around to see what was going on with the parameter indicated to be the problem. I thought I found the issue, but when I addressed it by constraining a residual variance across sites, the error message appeared again and implicated a different set of variables.

Should I take this error message as a warning (as you have indicated to others)? Or is this a real problem that I need to dig deeper to understand and address? How do I determine which it is?

This message refers to the fact that independence of observations is the number of clusters minus the number of strata with more than one cluster. This must be 21 in your case. You should not have more than 21 parameters in your model. This is like having more parameters than your sample size. This is not good practice.

I have a simple model with 4 variables: Y and X1 continuous, X2 and X3 binary. I have missing values on Y and would like to benefit FIML. My model statement is as follow Y on X1 X2 X3; [X1 X2 X3]; But I have the error message related to “non-positive definite first-order derivative product matrix.” In previous models with more continuous dependent variables, everything worked well. I wonder if there is any restriction on the nature of independent variables when using FIML? Can we still use FIML if all the Xs are categorical? In the specific case above what can I do? (I ve checked the parameter involved everything seems ok) Thank you

I am receiving an error when running alpha coefficients, but cannot figure out why. Mplus refers to the variance of one of my items, but I don't see what the problem is. Can you please help?

THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES MAY NOT BE TRUSTWORTHY FOR SOME PARAMETERS DUE TO A NON-POSITIVE DEFINITE FIRST-ORDER DERIVATIVE PRODUCT MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS 0.213D-16. PROBLEM INVOLVING THE FOLLOWING PARAMETER: Parameter 27, STU1WE7 (equality/label)